1. Field of the Invention
The present invention relates to a machine translation system capable of automatically translating input sentences in a natural language to a target language.
2. Description of the Background
Conventional machine translation systems for automatically translating an input sentence in a natural language to a target language have the following functions:
(1) A semantic analysis function: a function of selecting a semantically correct parse tree from among a plurality of parse trees obtained as a result of syntactic analysis performed on an input sentence, wherein the selection of the parse tree utilizes semantic restriction information described in a dictionary for both a word of restricting-side (i.e., a word accompanied by a case and imposing various restrictions on the case, like a verb) and another word of restricted-side (a word serving as the case for the restricting-side word and subjected to various restrictions from the restricting-side word, like a noun); PA0 (2) A divisional translation function: a function of generating translations individually for partial parse trees (partial trees) when a perfect syntactic analysis tree that would cover a whole sentence cannot be obtained by syntactic analysis, as is the case with an imperfect input sentence in which some indispensable case is omitted or with an input sentence including a syntactic structure that does not meet syntactic analysis rules; PA0 (3) A semantic attribute imparting function: a function of, when the restricted-side word is a registered word of a user dictionary or a technical term dictionary, or an unregistered word, imparting a passable, generic semantic attribute close to the concept in the source language to the restricted-side word, so that the semantic analysis will not fail; and PA0 (4) A learning function: a function of preferentially adopting a preselected or once-selected equivalent word, when a word in an input sentence has a plurality of equivalent words in the target language. PA0 (a) In the semantic analysis function in (1) above, if the semantic restrictions for excluding parse trees each forming a non-sentence from the obtained parse trees are too strict, all of the parse trees obtained may fail. Thus, there is a problem that too strict semantic restrictions cannot be used; PA0 (b) In the divisional translation function in (2) above, translations are generated individually for particular syntactically imperfect partial trees. In this case, even if a translation is generated, the translation is sometimes peculiar and hard to understand; PA0 (c) In the semantic attribute imparting function in (3) above, when the restricted-side word in an input sentence is a registered word of the user dictionary or the technical term dictionary or a registered word, the passable, generic "semantic attribute" would be imparted to the word as its semantic attribute. Accordingly, the semantic analysis of a sentence structure including such a word would lack in accuracy and therefore is not effective. Still more, in translating a document in which such words frequently occur, the semantic analysis may involve occurrence of noise, undesirably; and PA0 (d) In the learning function in (4) above, it is only when the parse tree of the input sentence coincides with the parse tree obtained during the learning process that a preferential equivalent word is adopted for translation and reflected upon the translation result.
By having these functions, the machine translation systems are enabled to obtain a translation even if a plurality of syntactic analysis trees (i.e., parse trees) have resulted, or if a syntactic analysis tree that will cover the whole input sentence cannot be obtained, or if the input sentence includes any technical word or any word that has not been registered in the dictionary and all the syntactic analysis trees have failed as a result of such semantic analysis as described in paragraph (1) above.
However, the above-listed functions of the machine translation systems have the following problems: